This method of this invention includes an algorithm which has been implemented in the Robotic Deriveting and Drilling work cell, an automatic fastener removal system currently being developed for use within the aerospace structural repair industry by Westinghouse Electric Corporation. A similar system is described in an article entitled Location and Identification of Rivets By Machine Vision, By Franke, Michalsky and McFalls, presented at Vision '85, Mar. 25-28, 1985, Society of Manufacturing Engineers, Dearborn, Mich. The Franke et al paper is included in the disclosure information statement submitted with this application.
A major difficulty in identification of the fastener type lies in the fact that there were five different types of fasteners, which may be grouped into three general classes, and hence there are three different spatial signals to differentiate, but each signal may be located in a different position, depending on fastener type. These fasteners were classified as having 2, 1 or no slots. The system described by Franke et al solves the problem of variability of signal location by requiring a priori knowledge of the expected fastener type. Because the information required to differentiate fastener type is contained in different parts of the image depending upon the fastener type being examined, the Franke et al system uses knowledge of the expected fastener type to decide where in the image to gather intensity data. The method disclosed in accordance with this invention solves this problem without requiring information regarding the expected fastener type. This method accepts a uniform template of data, equally spaced concentric circles of intensity data, from any fastener independent of type. The method then is able to intelligently decide which of these signals contains the pertinent information which is to be processed further.